ArticleDatabase : the journal of biological databases and curation2018
To increase trust, change the social design behind aggregated biodiversity data.
Article in Database : the journal of biological databases and curation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- A user-centric framework for harmonizing scientific name usage.Biodiversity data journal · 2026Article
- A scalable exemplar-based method for aligning biological taxonomies.Biodiversity data journal · 2026Article
- How many mammal species are there now? Updates and trends in taxonomic, nomenclatural, and geographic knowledge.Journal of mammalogy · 2025Article
- Unified and pluralistic ideals for data sharing and reuse in biodiversity.Database : the journal of biological databases and curation · 2023Article
- Article
- Paying it forward: Crowdsourcing the harmonisation and linking of taxon names and biodiversity identifiers.Biodiversity data journal · 2023Article
- Ten years and a million links: building a global taxonomic library connecting persistent identifiers for names, publications and people.Biodiversity data journal · 2023Article
- Using expert-elicitation to deliver biodiversity monitoring priorities on a Mediterranean island.PloS one · 2022Article
- Open Data Practices among Users of Primary Biodiversity Data.Bioscience · 2021Article
- From Reductionism to Reintegration: Solving society's most pressing problems requires building bridges between data types across the life sciences.PLoS biology · 2021Article
- Wanted: Standards for FAIR taxonomic concept representations and relationships.Biodiversity Information Science and Standards · 2021Article
- Decentralized but Globally Coordinated Biodiversity Data.Frontiers in big data · 2020Article
- Text-mined fossil biodiversity dynamics using machine learning.Proceedings. Biological sciences · 2019Article
- Verbalizing phylogenomic conflict: Representation of node congruence across competing reconstructions of the neoavian explosion.PLoS computational biology · 2019Article
- Ozymandias: a biodiversity knowledge graph.PeerJ · 2019Article
- Connecting data and expertise: a new alliance for biodiversity knowledge.Biodiversity data journal · 2019Article
- An updated checklist of the Tenebrionidae sec. Bousquet et al. 2018 of the Algodones Dunes of California, with comments on checklist data practices.Biodiversity data journal · 2018Article
- Article
Corrections and comments
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Authors and funding
2 authors.
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Abstract
Growing concerns about the quality of aggregated biodiversity data are lowering trust in large-scale data networks. Aggregators frequently respond to quality concerns by recommending that biologists work with original data providers to correct errors 'at the source.' We show that this strategy falls systematically short of a full diagnosis of the underlying causes of distrust. In particular, trust in an aggregator is not just a feature of the data signal quality provided by the sources to the aggregator, but also a consequence of the social design of the aggregation process and the resulting power balance between individual data contributors and aggregators. The latter have created an accountability gap by downplaying the authorship and significance of the taxonomic hierarchies-frequently called 'backbones'-they generate, and which are in effect novel classification theories that operate at the core of data-structuring process. The Darwin Core standard for sharing occurrence records plays an under-appreciated role in maintaining the accountability gap, because this standard lacks the syntactic structure needed to preserve the taxonomic coherence of data packages submitted for aggregation, potentially leading to inferences that no individual source would support. Since high-quality data packages can mirror competing and conflicting classifications, i.e. unsettled systematic research, this plurality must be accommodated in the design of biodiversity data integration. Looking forward, a key directive is to develop new technical pathways and social incentives for experts to contribute directly to the validation of taxonomically coherent data packages as part of a greater, trustworthy aggregation process.
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